As recent text-to-speech (TTS) systems have been rapidly improved in speech
quality and generation speed, many researchers now focus on a more challenging
issue: expressive TTS. To control speaking styles, existing expressive TTS
models use categorical style index or reference speech as style input. In this
work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that
utilizes a style tag written in natural language. Using a style-tagged TTS
dataset and a pre-trained language model, we modeled the relationship between
linguistic embedding and speaking style domain, which enables our model to work
even with style tags unseen during training. As style tag is written in natural
language, it can control speaking style in a more intuitive, interpretable, and
scalable way compared with style index or reference speech. In addition, in
terms of model architecture, we propose an efficient non-autoregressive (NAR)
TTS architecture with single-stage training. The experimental result shows that
ST-TTS outperforms the existing expressive TTS model, Tacotron2-GST in speech
quality and expressiveness.

本文提出了一种使用自然语言编写的样式标记的新型情感语音合成模型 StyleTagging-TTS，并使用预训练的语言模型对语言嵌入和说话风格域之间的关系进行建模，实现了对未见过的样式标记的控制。相比目前的表情 TTS 模型，该模型表现出更好的语音质量和表现力。